Chapter 5
In Pursuit of the Mechanical Man

Robot soldiers in any form may be decades away, but that task is simple
compared with the skills and efforts needed to produce a robot that could
be mistaken for a real human. Creating a humanoid robot is the ultimate
goal for many AI researchers, and the most daunting. A convincing humanoid
robot would have to walk, gesture, and maneuver as easily as a human,
understand the spoken language, be able to communicate, and perhaps even
be able to feel emotions and respond to feelings. These are just some of
the challenges that AI researchers in labs all over the world must
consider.

The scientific research in pursuit of the mechanical man is scattered.
Researchers tend to specialize in only one small area of humanoid robotic
operation such as speech recognition, visual perception, or mobility, each
of which is a highly technical, complex discipline in itself. This is an
enormously costly endeavor with an uncertain timeline. For many years,
Japan has led the research in humanoid robotics because, as Hirohisa
Hirukawa of Japan's National Institute of Advanced Industrial
Science and Technology says, "We are confident that the long-term
future of humanoid robots is bright."
17

Why Make It Look Human?

Researchers already know how to make machines that are sturdy enough to be
dropped onto another planet, smart enough to run businesses, and precise
enough to perform surgery. So why would scientists go to all the trouble
of making a robot look like a person? This question is hotly debated. Some
scientists believe there is no reason. So far, nonhumanoid robots perform
better than those with humanoid designs, and it is less expensive to
create machines that maneuver on several legs or on wheels or treads. Some
researchers raise ethical concerns that if robots look too human there may
be the potential for abuse. "Robots need to be designed so that
they will be regarded as appliances rather than as people,"
18
says John McCarthy of Stanford University. People may treat humanoid
robots as slaves,

David Hanson sculpted humanlike features for his robotic head,
Hertz, and programmed it to exhibit realistic facial expressions.

and that is one relationship that ethicists fear could carry over to
interpersonal relationships.

The Creep Factor

Kismet's face hardly resembles a human's, but it is cute
and it does attract an observer's attention because of its
lifelike expressions. David Hanson, who worked for Walt Disney Imaging
as a sculptor-roboticist, is one researcher who is working on putting a
more realistic face on future robots. He created a high-tech polymer
plastic that resembles human skin, which he calls f'rubber. With
it he created a very human-like face over a robotic head. So far powered
only by a laptop computer, the head, called Hertz, can bat its eyes and
ask questions. Hertz has twenty-eight very realistic-looking facial
movements, including smiling, sneering, and furrowing its brow. But a
robot who looks too real can cause problems. Tests showed that a robot
with a face that was too realistic gave people the creeps and actually
decreased the robot's effectiveness.

People find the robot Kismet's lifelike expressions cute,
and not creepy at all, perhaps because Kismet does not resemble a
human.

The other side in this debate is strongly in favor of a robot that looks
like a person, especially if it were to work with people in a home or work
environment. "If you build a robot that people have a short-term
interaction with, you'd better make it connect with things people
are familiar with,"
19
says Stanford University professor Sebastian Thrun. People are more
likely to interact with a robot that is designed like themselves than a
robot with an alien shape and design. There is also the belief that the
essence of intelligence is a combination of mind and body. Japanese
robotics expert Fumio
Hara believes that a robot would not be completely effective without the
embodiment of a humanlike presence. So what elements are essential in
order to make a robot humanlike? It needs to have a familiar face,
identifiably humanlike behaviors, appropriate social interactions, and of
course a physical form that stands upright and is capable of walking on
two legs.

Walk This Way

Almost all children are toddling around on two legs by the age of two.
This physical attribute took millions of years to evolve in primates.
Honda, one of the largest industrial companies in the world, spent
millions of dollars but only ten years of top-secret research to
effectively duplicate this movement in a machine.

Walking upright is extremely difficult to duplicate and requires a lot of
AI computing power. It requires balance, coordination, muscle power, and
flexibility just to take three steps across a smooth tile floor in a
straight line. But stepping out into an unfamiliar rocky terrain with many
obstacles in a robot's path requires even more AI power to adjust a
step, alter foot placement, and register ground resistance—things
people do without conscious thought.

Honda's robot, ASIMO (Advanced Step in Innovative Mobility), is
able to walk backward and sideways, go up and down stairs, and even play
soccer. ASIMO looks like a small child traipsing around in a white plastic
space suit. ASIMO is only four feet tall, but it can simulate human
locomotion and use its arms in a realistic fashion. Its designers made
special efforts to make it cute so that it was not perceived as
threatening and would be more easily accepted. In 2003 ASIMO marched into
a state dinner attended by the prime ministers of Japan and the Czech
Republic. ASIMO shook hands with Prime Minister Vladimir Spidla and placed
a bouquet of flowers at the base of a statue honoring science fiction
author Karel Capek, who coined the term
robot
in 1920.

ASIMO is a tiny humanoid robot that can simulate human locomotion
and can even play soccer.

Other humanoid robots have demonstrated their prowess at martial arts,
soccer, dancing, and even conducting an orchestra. The key to making a
robot athletic is simulating the muscle, bone, and nerves in machinery.
All robots make use of many of the same components: a jointed metal or
plastic skeletal frame and motors, pulleys, gears, and hydraulics to
provide the muscle power. But advances in polymer chemistry are changing
that. Researchers are experimenting with new materials such as EAP, or
electroactive polymer, to produce more realistic muscle power. EAP is a
rubbery plastic substance that works by changing shape when electricity is
applied to it. It can be made into bundles of fibers that are able to
shorten or lengthen, just like real muscles, when the fibers are attached
to a motor. The material also weighs less and is less likely to break down
than metal. But the most important element is the brainpower needed to
coordinate it all.

ASIMO carries its computer brain in a pack on its back. It has three
cameras (two on its head and one at its waist) that allow it to see and
chase a soccer ball. It also has sensors in each ankle to predict its next
step. Gravity sensors keep track of the force of each movement, and
solid-state gyroscopes monitor its body position and keep it upright. But
what keeps this petite robot upright and balanced while carrying out
complex movements are impressive AI algorithms programmed into its
circuitry. If ASIMO stands on one leg and swings its arm out to the side,
the program automatically adjusts and the robot moves its torso to keep
its balance.

Getting Around

Being able to move and walk on two legs is one accomplishment, but knowing
how to navigate is another. Using preprogrammed maps like Shakey the robot
used fifty years ago is no longer the way robots get around. Today genetic
algorithms direct robotic navigation and control systems so that a robot
can learn and adapt to any new environment. There are many navigation
programs under experimentation, but the most unusual uses pain as a
navigational tool.

Robots in this experiment were trained to seek out specific objects, grab
them, and transport them to a specific drop-off point. The
experiment's designer, Matthew Hage of the Lawrence Livermore
National Laboratory, influenced the robot's choice of route by
programming it to "feel" pain. When the robot bumped into a
physical object, it "hurt" from the damage it suffered. If
it came close to a hot spot, a place where radiation emanated, the robot
also associated it with pain and kept its distance. The robot's
task, then, was to follow the least painful path.

Sensory Perception

Few robots are equipped to perceive stimuli as pain, but sensory
perception is key to making an effective
humanoid robot or any other artificial intelligence. Humans experience
the world through five senses. In order for a robot to interact with
humans effectively, it has to be able to experience what humans
experience. Without the senses of hearing, touch, sight, taste, and smell,
people would not be able to act fully within their environment. Even Alan
Turing felt that perception was important in his early theoretical design.
In his paper "Computing Machinery and Intelligence" he
suggested, "It is best to provide the machine with the best sense
organs that money can buy and then teach it to understand and speak
English. This process could follow the normal teaching of a child."
20

Perception and thinking are the respective functional correlates of the
sensory organs and the brain. In order to learn the most from its
environment, the human brain fine-tunes how and what a person senses.
Giving machines the chance to perceive the world through similar, if not
better, sensory organs allows them a chance to understand the world as
humans do. Otherwise they would simply be programmed machines incapable of
learning.

Some of the earliest perception systems were designed to recognize
language, that is, identify characters and words that make up text. Once
the language was perceived, the machine would convert it into a coded
form. For example, most search engines today operate using Optical
Character Recognition Systems to read typed-in information. Understanding
what that information meant, however, was limited to the context of the
word or symbol. What at first looked like a simple exercise to create a
machine that could see and recognize symbols became an exploration into
how humans perceive and understand the world.

Artificial Vision

Many aspects of human sensory perception are difficult if not impossible
to duplicate. The human eye, for example, is an incredibly complex
structure that

With its four camera "eyes," Cog, an android developed
at MIT, sees an object from four distinct perspectives.

provides frontal and peripheral vision; a pair of eyes also provides depth
perception and better form recognition. The eyes can lock on to a moving
object and follow it in one smooth motion or move in a stop and start
motion from object to object so that vision is not blurry as a
person's eyes move around. The eyes register the level of light and
measure depth and distances of objects. The eyes sift through a lot of
information before any encoded messages are sent to the brain, where 60
percent of the cerebral cortex is devoted to processing visual
information. Scientists know how the eye operates—the mechanics of
the eye and its movements can be duplicated—but scientists know
much less about how eye-brain perception works. Duplicating that
connection is more difficult.

To simulate eye-brain perception, a robot needs several cameras operating
at the same time. The cameras
must be connected to a neural network that can sift through data to pass
on the pertinent information to higher levels of network. Mathematical
algorithms convert patterns of color intensities and turn them into
descriptions of what appears before the cameras. Computer vision is
capable of detecting human faces, locating eyes in a face, tracking
movement, and registering various shapes and colors. But it is not as good
at recognizing whether a face is male or female, determining the direction
of a person's gaze, or recognizing the same person who appears
later with a hat or beard, or recognizing the difference between a cup and
a comb. Those distinctions come from a higher level of perception that is
still being explored. A robot's view of the world is best described
by Rodney Brooks in his book
Flesh and Machines
as "a strange, disembodied, hallucinatory experience."
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Artificial Ears

Like eyes, ears are complex organs. The human ear is capable of
registering and identifying thousands of different sounds. A person can
determine what made the sound and how far away the source of the sound may
have been. Microphones that operate as a robot's ears only receive
the sound. The perception comes in the form of complex AI programs that
register sound and match it to a stored catalog of recognizable noises.

Human ears receive sound in waves, which are converted into electrical
impulses that are sent to the brain. This conversion is done in the
cochlea of the ear, where the sound waves resonate and trigger the
movement of tiny hairs called cilia, which fire the attached neural cells.
An artificial ear operates in much the same way. Locating the origin of a
sound is done through measuring the infinitesimal differences in time
between the waves reaching two different microphones. Speech recognition
is achieved through the use of sophisticated pattern recognition software.

Touch

The sense of touch is also very complicated. Large industrial robotic arms
are dangerous when they maneuver with great force; a humanoid robot would
need to have a delicate sense of touch so that if it bumped into a person,
the robotic arm would recoil automatically without harming the human.

A humanoid robot would also have to be able to use human tools and grasp
and hold objects. Robotic hands are designed with special strain gauges
that measure the amount of pressure needed to pick up an object and
contact switches that simulate touch and grasping motions. When a switch
comes in contact with something, it closes and sends a signal to the
computer brain. The strain gauges record the appropriate amount of
pressure needed to pick up the object, making it possible for the same
robotic hand to pick up a hammer one minute and a fragile egg the next.

Extrasensory Perception

The advantage of an artificial system is that it can be enhanced with
extrasensory perception. For example, an artificial nose can be enhanced
to far exceed the sensitivity of a flesh-and-blood nose. Called neural
noses, these microchips are so sensitive that they can detect smells
that humans are not even aware of. Artificial noses are used in airports
to detect explosive materials and narcotics and may be used in
diagnosing cancer by smelling precancerous tissues. Incorporating this
type of extrasensory perception into a humanoid robot would give it an
advantage over its human counterpart. Military robots, for example,
might be fitted with chemical noses or infrared night vision that would
tell them when a living being was nearby.

NASA has engineered one of the more dexterous robots, called Robonaut, to
perform dangerous construction work on the space station. The prototype
had to be designed to use finer motor skills than a space-suited astronaut
would have. Each arm attached to Robonaut's Kevlar body contains
more than 150 sensors that are connected to an artificial spinal cord
through which sensory signals flow quickly back and forth to the computer
brain. Its hands are capable of opening a can of soda, cutting with
scissors, and handling tools commonly used on the space station.

Researchers in Japan are also working on a way to make robot hands as
sensitive as a human hand. A rubbery pressure-sensing membrane laminated
onto a flexible layer of plastic transistors creates a primitive
artificial skin. When the fake skin is touched with the metal tip of a
rod, it generates a weak electrical signal, which is then sent to
receivers in the computer brain and registers as a touch.

Educating Cog

A robot that can touch, see, and hear is ready to experience the world as
a human does. One robot that embodies Alan Turing's idea of
allowing a robot to learn like a child through sensory perception is Cog
(from the Latin word
cogitare
, "to think"). A human infant learns through trial and error
as it encounters each new aspect of its environment. Each new piece of
information it learns is filed away and used as a basis for more
experiences and learning opportunities. Cog, created at MIT more than ten
years ago, learns the same way but is still no more knowledgeable than a
six-month-old baby.

Unlike Deep Blue and other expert systems, Cog was not programmed to do
much of anything. It must learn all the necessary data it needs by
experiencing the world around it. It learns to move and react by watching
its trainer's movements and reactions. Cog's bulky metal
frame and face hide the fact that it is just a baby. Like an infant, it
can track movement with its camera eyes and move its head to keep an
object in view. It can recognize some faces, detect eye contact, and react
to sounds.

This imitative learning takes time, but it is approaching self-directed
learning because it allows interaction between human and robot. Cog can
ask

Cog creator Rodney Brooks hopes that his android will teach
researchers more about the way people learn by interacting with
others.

questions or request that a movement be repeated over and over. Some
robots have even shown frustration and boredom when the learning process
gets difficult.

Emotions

Down the hall from Cog is its cousin, Kismet. Inspired by Cog's
infantile behavior, researcher Cynthia Breazeal created Kismet, one of the
most sociable robots. Whereas
no one treats the hulkish Cog like a baby, people frequently use baby
talk with Kismet. A cartoonish head bolted to a table, Kismet will respond
to a person's approach, waggle its fuzzy, caterpillar-like
eyebrows, and turn up its red licorice-whip lips in a grin. The underlying
premise of Kismet is that emotions are necessary to guide learning and
communication.

Because most information exchanged between humans is done through facial
expressions, it is important to give a robot facial expressions to make
robot-human interactions as informative as possible. A human infant will
smile and try to attract the attention of its mother. When that is
achieved, it will follow the mother's

movements and try to engage in play. Kismet can do the same. As a baby is
motivated by hunger or thirst, Kismet is motivated by stimulation. It is
programmed with a social, or stimulation, drive, which means it seeks out
experiences that will stimulate it. But it also has a fatigue syndrome,
which means it gets tired over time. The goal is for Kismet to keep these
two drives in balance and learn what works and what does not in social
situations. "The robot is trying to get you to interact with it in
ways that can benefit its ability to learn,"
22
says Breazeal.

For example, Kismet is programmed to seek out social stimulation. Its
bright blue eyes are always looking around for movement, bright colors,
skin tones, and face shapes. The images it takes in are processed through
a neural network that recognizes faces and their expressions. Its large
pink ears listen for voices. When it senses a person is near, it will try
to attract the person with facial expressions and baby talk. If an
expression works and a person passing by stops to talk, Kismet's
internal social drive program is satisfied. If the expression does not
work, the internal social drive level sinks and prompts Kismet to try
something different. Kismet also knows how to react to stimulation it
perceives negatively. If a person gets too close to its face, Kismet will
sense an invasion of space and either mimic an expression of annoyance or
turn away.

Breazeal programmed Kismet with a repertoire of seven basic facial
movements, but she theorizes that the more Kismet interacts with humans,
the more it will learn and refine those expressions and add to them. Its
impressive array of facial expressions is controlled by fifteen external
computers along one wall, with no one computer in control.

Putting All the Pieces Together

All of these components—bipedalism, sensory perception, and facial
expressions—have yet to be put together into an effective and
convincing mechanical

British researcher Steve Grand built Lucy to test his theories about
sensory perception and artificial intelligence.

man. There are no robots that combine the mobility of ASIMO, the
sociability of Kismet, and the chess-playing ability of Deep Blue. Many of
the skills that humans take for granted like running, enjoying music, or
recognizing objects are still beyond the abilities of even the most
advanced robots, but AI researchers are working on them piece by piece.
The potential is there. No one can determine a date in the future when
humanoid robots will babysit children or assist the elderly, but hope is
high and the technology is progressing.

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